pyPhenology.models.Model.score

Model.score(metric='rmse', doy_observed=None, to_predict=None, predictors=None)

Evaluate a prediction given observed doy values

Given no arguments this will return the RMSE on the dataset used for fitting (if fitting was done). To evaluate a new set of data set to_predict, and predictors as used in model.predict(). The predictions from these will be evluated against the true values in doy_observed.

Metrics available are root mean square error (rmse) and AIC (aic). For AIC the number of parameters in the model is set to the number of parameters actually estimated in fit(), not the total number of model parameters.

Parameters:

metric : str, optional

The metric used either ‘rmse’ for the root mean square error, or ‘aic’ for akaike information criteria.

doy_observed : numpy array, optional

The true doy values to evaluate with. This must be a numpy array the same length as the number of rows in to_predict

to_predict : dataframe, optional

pandas dataframe of site/year combinations to predict from the given predictor data. just like the observations dataframe used in fit() but (optionally) without the doy column

predictors : dataframe, optional

pandas dataframe in the format specific to this package

Returns:

The score as a float